Video SAR Moving Target Indication Using Deep Neural Network

Video synthetic aperture radar (SAR) has been found to be very valuable for detection and tracking of slow moving targets and for observing changes over short time periods. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. This article presents a framework for shadow-aided moving target detection using deep neural networks (DNNs) in video SAR. The faster region-based convolutional neural network (Faster-RCNN) is first used to detect shadows in a single frame, and then all the detections in each frame are filtered by the improved density-based clustering algorithm to mitigate false alarms. The missing alarm in detection can be suppressed by using the designed bi-directional long-short-term memory (Bi-LSTM) network. The proposed approaches have been tested by both the simulated and the real data, and a comparison to classical processing is given. This article reveals that the DNN techniques are very suitable for video SAR moving target detection.